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Improving the arbovirus surveillance and control program's management practices: developing an integrated data platform using Big Data, analysis models, and data visualization

Grant number: 23/10080-3
Support Opportunities:Research Grants - Research in Public Policies
Duration: April 01, 2024 - March 31, 2028
Field of knowledge:Health Sciences - Collective Health - Public Health
Principal Investigator:Francisco Chiaravalloti Neto
Grantee:Francisco Chiaravalloti Neto
Host Institution: Faculdade de Saúde Pública (FSP). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Associated researchers:Alexandre Cláudio Botazzo Delbem ; Andreia Francesli Negri ; Antonio Mauro Saraiva ; CAMILA SANTANA DE OLIVEIRA ; FERNANDO AUGUSTO GARCIA ; Francisca Leiliane de Oliveira Mota ; Gabriel Henrique Pavanette ; Gerson Laurindo Barbosa ; Gleice Margarete de Souza Conceição ; Isabela Pires de Almeida ; Leandro Manuel Reis Velloso ; Lidia Maria Reis Santana ; Lucia Vilela Leite Filgueiras ; Manuela Gallo y Sanches ; Márcia Ito ; Maurício Lacerda Nogueira ; Paulo Cesar dos Anjos Gasques ; Pedro Luiz Pizzigatti Corrêa ; Solange Nice Alves de Souza


The project seeks to promote advances in knowledge and improvement based on current and ongoing public policies related to the control of urban arboviruses on data analysis that aim to improve decision-making promptly and in a precise and effective manner.The national guidelines define the responsibilities concerning urban arboviruses, the State monitors and evaluates the municipalities for the fulfillment of the established goals and objectives, and the cities, in turn, within their territory, have the responsibility of managing this surveillance, as well as invest in its implementation, establishing monitoring and evaluation indicators.One of the municipality's premises is to facilitate the integration of all actions in its territory, encouraging the participation of people and areas and focusing on work processes in the search for results.Considering that there is an enormous amount of raw data in the municipality, it is necessary, for the best decisions, that the manager has correct and quality information at hand. And this information depends on adequate integration between the banks that provide the data for analysis.Based on this, this project aims to create a platform based on Big Data technologies that functionally integrates databases related to urban arboviruses, incorporating tools such as artificial intelligence and predictive models in the analysis of consistency and coherence of information and visualization of data.This multidisciplinary and inter-institutional project will use qualitative and quantitative methods and data science, looking for patterns and indicators. Then, it will also allow the construction of explanatory and predictive models to guide public policies.The methodology used in the project will consist of three sub-themes: Big Data (data integration and management), Predictive and explanatory models (assessment and analysis of arboviral transmission), and Infovis Area (integration and visualization of information for users and managers). The scientific results that will be obtained are data platform; maps with the profile of the frequency of transmission of arboviruses; the relationship between disease transmission and its influencing factors; thematic maps of cases of arboviruses representing the incidence rates of arboviruses in space and time; decision support algorithms; applications for the population; and dashboards for viewing arboviral data.The results will be disseminated in meetings and seminars with managers, showing the main results and challenges and discussing their application in the context of the program, in addition to dissemination through publications in high-impact scientific journals and congresses. Implementing what is proposed in this project at the local level will bring benefits related to innovations in surveillance strategies for urban arboviruses in the municipality, with the possibility, once successful, of expanding and incorporating the products obtained as a state and federal public policy.The organization of this project foresees phases with the active participation of the different teams involved in each one of the phases and overlapping and exchange of knowledge throughout the process. With an emphasis on multi and interdisciplinarity, the collaborating institutions will work within their expertise, directing efforts towards questions about the public management processes. Thus, in the medium and long term, an integrated health data warehouse can be developed, allowing advanced queries and analysis and providing a sophisticated health intelligence panel to support the elaboration of evidence-based policies for public management processes. (AU)

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